PGDBA Interview- Which Topics to Cover and How?

With the stress for PGDBA interview, chances are that you may end up getting nervous while actually giving the interview. To avoid any failures, you must leave no stone unturned while preparing. To help you out with the admission procedure, we bring you a set of topics that you should prepare well for your interview.

While receiving queries regarding the admission to PGDBA, topics to be studied for the interview had been the most common. Aspirants are concerned about the level of Mathematics or the level of Statistics that they should study for the interview. Here, we are listing down a few topics that you should prepare for your interview.

Probability

1. Concept of Probability, Sample Space, Independent Events, Disjoint Events, etc.
2. Bayes Theorem
3. Random Variables,  for example- Discrete/Continuous Random Variables; Expectation and Variance of Random Variables; Probability Density Function and Probability Distribution Function for various common distribution like Uniform, Binomial, Bernoulli, Normal (Gaussian)
4. Concept of Sample Mean, Law of Large Numbers and Central Limit Theorem

Statistics

1. Different types of Data/Variables, for example- Numeric, Interval, Categorical, Nominal, etc.
2. Measures of Central Tendency and Variance like Mean, Median, Mode, Quantiles, Standard Deviation and Variance; Difference between these summaries and which of these is more representative of the central tendency for which kind of data and for what application; What’s a skewed distribution, for example- Left v/s Right Skewed and how is Median and Mean different for these distribution
3. Concept and Definition of Covariance and Correlation (for example- Pearson’s Correlation); the difference between Correlation and Causality
4. What’s Regression Analysis and the difference between Linear and Logistic Regression
5. Concept of Statistical Inference, Confidence Interval, Hypothesis Testing and some
6. Statistical Tests, for example- Z-Test and t-Test

Linear Algebra

1. System of Linear Equations: Conditions for Independence & Consistency, Conditions for No Solution, Unique Solution & Infinite Solutions
2. Matrices & Determinants
3. Basics of Vector Spaces: Linear Combination, Linear Span, Linear Independence, Basis Function, Linear Subspace

Machine Learning

1. Types of Learning: Supervised, Unsupervised, Semi-Supervised and Reinforcement Learning
2. Examples of Supervised and Unsupervised learning: Regression, Classification, and Clustering
3. Basic Idea about some fundamental methods for each of these learning methods like Linear Regression, Logistic Regression, and K-Means Clustering

Calculus

1. Basics of Limits and Differentiation
2. Critical or Stationary Points like Minima, Maxima and Inflexion Points
3. Basics of Integration

It must be noted that the comprehensibility of these topics cannot be assured due to the personal nature of the interviews. For instance, if you have highlighted exposure to any particular domain in your application, you should be well prepared for those too.

You may also learn from the real experiences of students from the course here.